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Volumn , Issue , 2010, Pages 417-426

Using fine-grained skill models to fit student performance with bayesian networks

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EID: 84858972224     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/b10274     Document Type: Chapter
Times cited : (34)

References (21)
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  • 4
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    • Ayers, E. and Junker, B. W. (2006). Do skills combine additively to predict task difficulty in eighth grade mathematics? In Beck, J., Aimeur, E., and Barnes, T. (eds.), Educational Data Mining: Papers from the AAAI Workshop. Menlo Park, CA: AAAI Press. Technical Report WS-06-05, pp. 14-20.
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    • Ayers, E.1    Junker, B.W.2
  • 5
    • 33646031770 scopus 로고    scopus 로고
    • Q-matrix method: Mining student response data for knowledge
    • Pittsburgh, PA, 2005. AAAI Technical Report #WS-05-02
    • Barnes, T. (2005). Q-matrix method: Mining student response data for knowledge. Proceedings of the AAAI-05 Workshop on Educational Data Mining, Pittsburgh, PA, 2005. AAAI Technical Report #WS-05-02.
    • (2005) Proceedings of the AAAI-05 Workshop on Educational Data Mining
    • Barnes, T.1
  • 6
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    • Introducing prerequisite relations in a multi-layered Bayesian student model
    • Ardissono, L., Brna, P., and Mitrovic, A. (eds.), Lecture Notes in Computer Science, Springer, Berlin, Germany
    • Carmona, C., Millán, E., de-la Cruz, J.-L.P., Trella, M., and Conejo, R. (2005). Introducing prerequisite relations in a multi-layered Bayesian student model In Ardissono, L., Brna, P., and Mitrovic, A. (eds.), User Modeling. Lecture Notes in Computer Science, Vol. 3538. Springer, Berlin, Germany, pp. 347-356.
    • (2005) User Modeling. , vol.3538 , pp. 347-356
    • Carmona, C.1    Millán, E.2    De-La Cruz, J.-L.P.3    Trella, M.4    Conejo, R.5
  • 8
    • 0007297658 scopus 로고
    • Student modeling in the ACT programming tutor
    • Nichols, P., Chipman, S., and Brennan, R. (eds.), Erlbaum, Hillsdale, NJ
    • Corbett, A. T., Anderson, J. R. & O’Brien, A. T. (1995). Student modeling in the ACT programming tutor In Nichols, P., Chipman, S., and Brennan, R. (eds.), Cognitively diagnostic assessment. Erlbaum, Hillsdale, NJ, pp. 19-41.
    • (1995) Cognitively diagnostic assessment. , pp. 19-41
    • Corbett, A.T.1    Anderson, J.R.2    O’Brien, A.T.3
  • 9
    • 33846010533 scopus 로고    scopus 로고
    • Using mixed-effects modeling to compare different grain-sized skill models
    • Beck, J., Aimeur, E., and Barnes, T. (eds.), AAAI Press. Technical Report WS-06-05. ISBN 978-1-57735-287-7
    • Feng, M., Heffernan, N. T., Mani, M., and Heffernan, C. (2006). Using mixed-effects modeling to compare different grain-sized skill models In Beck, J., Aimeur, E., and Barnes, T. (eds.), Educational Data Mining: Papers from the AAAI Workshop. AAAI Press. Technical Report WS-06-05. ISBN 978-1-57735-287-7, pp. 57-66.
    • (2006) Educational Data Mining: Papers from the AAAI Workshop. , pp. 57-66
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  • 11
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    • Granularity-based reasoning and belief revision in student models
    • Greer, J. E. and McCalla, G. I. (eds.), Springer-Verlag, Berlin, Germany
    • McCalla, G. I. and Greer, J. E. (1994). Granularity-based reasoning and belief revision in student models In Greer, J. E. and McCalla, G. I. (eds.), Student Modelling: The Key to Individualized Knowledge-Based Instruction. Springer-Verlag, Berlin, Germany, pp 39-62.
    • (1994) Student Modelling: The Key to Individualized Knowledge-Based Instruction. , pp. 39-62
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    • Mislevy, R.J.1    Gitomer, D.H.2
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    • The composition effect: Conjunctive or compensatory? An analysis of multi-skill math questions in ITS
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    • Pardos, Z. A., Heffernan, N. T., Ruiz, C., and Beck, J. (2008). The composition effect: Conjunctive or compensatory? An analysis of multi-skill math questions in ITS. Proceedings of the First Conference on Educational Data Mining, Montreal, Canada, pp. 147-156. http://ihelp.usask.ca/iaied/ijaied/AIED2007/AIED-EDM_proceeding_full2.pdf
    • (2008) Proceedings of the First Conference on Educational Data Mining , pp. 147-156
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.